| Literature DB >> 32837280 |
Satinder Kaur1, Hemant Bherwani2,3, Sunil Gulia4, Ritesh Vijay2, Rakesh Kumar2.
Abstract
COVID-19 is a highly infectious disease caused by SARS-CoV-2, first identified in China and spread globally, resulting into pandemic. Transmission of virus takes place either directly through close contact with infected individual (symptomatic/asymptomatic) or indirectly by touching contaminated surfaces. Virus survives on the surfaces from few hours to days. It enters the human body through nose, eyes or mouth. Other sources of contamination are faeces, blood, food, water, semen etc. Parameters such as temperature/relative humidity also play an important role in transmission. As the disease is evolving, so are the number of cases. Proper planning and restriction are helping in influencing the trajectory of the transmission. Various measures are undertaken to prevent infection such as maintaining hygiene, using facemasks, isolation/quarantine, social/physical distancing, in extreme cases lockdown (restricted movement except essential services) in hot spot areas or throughout the country. Countries that introduced various mitigation measures had experienced control in transmission of COVID-19. Python programming is conducted for change point analysis (CPA) using Bayesian probability approach for understanding the impact of restrictions and mitigation methods in terms of either increase or stagnation in number of COVID-19 cases for eight countries. From analysis it is concluded that countries which acted late in bringing in the social distancing measures are suffering in terms of high number of cases with USA, leading among eight countries analysed. The CPA week in comparison with date of lockdown and first reported case strongly correlates (Pearson's r = - 0.86 to - 0.97) to cases, cases per unit area and cases per unit population, indicating earlier the mitigation strategy, lesser the number of cases. The overall paper will help the decision makers in understanding the possible steps for mitigation, more so in developing countries where the fight against COVID-19 seems to have just begun. © Springer Nature B.V. 2020.Entities:
Keywords: Bayesian probability; COVID-19; COVID-19 health impacts; Change point analysis; Pandemic; SARS-CoV-2; Social distancing
Year: 2020 PMID: 32837280 PMCID: PMC7368631 DOI: 10.1007/s10668-020-00884-x
Source DB: PubMed Journal: Environ Dev Sustain ISSN: 1387-585X Impact factor: 3.219
Weekly cumulative statistics of COVID-19 of selected countries
| Week | India | USA | France | Italy | Japan | Iran | Spain | China |
|---|---|---|---|---|---|---|---|---|
| 1 | 3 | 11 | 6 | 3 | 4 | 95 | 2 | 278 |
| 2 | 3 | 13 | 6 | 3 | 20 | 2336 | 2 | 2761 |
| 3 | 3 | 15 | 11 | 400 | 26 | 8042 | 2 | 17,238 |
| 4 | 3 | 53 | 12 | 3089 | 59 | 16,169 | 45 | 40,235 |
| 5 | 34 | 108 | 38 | 12,462 | 144 | 21,638 | 589 | 70,635 |
| 6 | 84 | 696 | 420 | 35,713 | 455 | 32,332 | 7753 | 77,262 |
| 7 | 315 | 3536 | 2860 | 74,386 | 1054 | 50,468 | 28,572 | 80,174 |
| 8 | 918 | 32,341 | 10,995 | 92,472 | 1693 | 58,226 | 83,749 | 81,439 |
| 9 | 3072 | 140,223 | 29,155 | 124,632 | 3271 | 62,589 | 138,587 | 81,669 |
| 10 | 7529 | 333,593 | 59,105 | 152,271 | 6748 | 73,303 | 171,981 | 82,052 |
| 11 | 14,792 | 553,493 | 86,334 | 175,925 | 10,361 | 82,211 | 202,693 | 82,735 |
| 12 | 24,942 | 750,718 | 108,847 | 195,351 | 13,182 | 89,328 | 228,610 | 82,827 |
| 13 | 37,776 | 1,126,250 | 120,804 | 209,328 | 14,839 | 95,646 | 248,028 | 82,877 |
| 14 | 59,662 | 1,300,243 | 129,581 | 218,268 | 15,747 | 104,691 | 264,670 | 82,901 |
Fig. 1Flow diagram explaining different modes of transmission of SARS-CoV-2
Fig. 2CPA results for each selected country
Significance of change point with respect to lockdown
| Country | Date of first case reporting (Dfr) | Lockdown date (Dl) | Case weeks before lockdown {CW = (Dl − Dfr)/7} | CP week (CPw) | Delta (D = CPw-CW) |
|---|---|---|---|---|---|
| India | 30 Jan | 25 Mar | 7.9 | 9 | 1.1 |
| USA | 13 Jan | 19 Mar | 9.4 | 8 | -1.4 |
| France | 24 Jan | 17 Mar | 7.6 | 7 | -0.6 |
| Italy | 31 Jan | 09 Mar | 5.4 | 6 | 0.6 |
| Japan | 04 Jan | 27 Feb | 7.7 | 9 | 1.3 |
| Iran | 19 Feb | 28 Mar | 5.4 | 4 | -1.4 |
| Spain | 31 Jan | 14 Mar | 6.1 | 7 | 0.9 |
| China | 31 Dec | 25 Jan | 3.6 | 6.5 | 2.9 |
CPA results in relation to cases and population
| Country | Delta (D) | Cases (C) | Population (P) | Population density (PD) | Cases/population (CPP) | Cases/density (CPD) |
|---|---|---|---|---|---|---|
| USA | − 1.43 | 1,300,243 | 33,10,02,651 | 36 | 0.00393 | 36,117.86 |
| Iran | − 1.43 | 104,691 | 8,39,92,949 | 52 | 0.00125 | 2013.29 |
| France | − 0.57 | 129,581 | 6,52,73,511 | 119 | 0.00199 | 1088.92 |
| Italy | 0.57 | 218,268 | 6,04,61,826 | 206 | 0.00361 | 1059.55 |
| Spain | 0.86 | 264,670 | 12,64,76,461 | 347 | 0.00209 | 762.74 |
| China | 2.93 | 82,901 | 1,43,93,23,776 | 153 | 0.00006 | 541.84 |
| Japan | 1.29 | 15,747 | 4,67,54,778 | 94 | 0.00034 | 167.52 |
| India | 1.14 | 59,662 | 1,38,00,04,385 | 464 | 0.00004 | 128.58 |
Fig. 3Importance of early action in control of COVID-19 spread
Correlation matrix for delta and population parameters
| D | 1.00 | |||||
| C | − 0.86 | 1.00 | ||||
| P | 0.36 | − 0.41 | 1.00 | |||
| PD | − 0.03 | 0.19 | 0.80 | 1.00 | ||
| CPP | − 0.97 | 0.83 | − 0.58 | − 0.21 | 1.00 | |
| CPD | − 0.97 | 0.89 | − 0.57 | − 0.14 | 0.99 | 1.00 |
Fig. 4Transmission of COVID-19 symptom, mitigation measures adopted and effect on population